



from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


UpperCAmelCase_		 =					logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCAmelCase_		 =					"""
    Examples:
        ```py
        >>> from PIL import Image
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif, load_image

        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")

        >>> repo = \"openai/shap-e-img2img\"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 3.0
        >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"
        >>> image = load_image(image_url).convert(\"RGB\")

        >>> images = pipe(
        ...     image,
        ...     guidance_scale=guidance_scale,
        ...     num_inference_steps=64,
        ...     frame_size=256,
        ... ).images

        >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")
        ```
"""


@dataclass
class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 a__					: Union[PIL.Image.Image, np.ndarray]


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 def __init__(    self					:					int			,						__lowerCAmelCase					:					PriorTransformer			,						__lowerCAmelCase					:					CLIPVisionModel			,						__lowerCAmelCase					:					CLIPImageProcessor			,						__lowerCAmelCase					:					HeunDiscreteScheduler			,						__lowerCAmelCase					:					ShapERenderer			,						)   ->	List[str]:
    super().__init__()

    self.register_modules(
        prior=__lowerCAmelCase			,						image_encoder=__lowerCAmelCase			,						image_processor=__lowerCAmelCase			,						scheduler=__lowerCAmelCase			,						renderer=__lowerCAmelCase			,						)

 def     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					Optional[Any]			,						__lowerCAmelCase					:					List[Any]			,						__lowerCAmelCase					:					Optional[int]			,						__lowerCAmelCase					:					List[str]			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					Tuple      )   ->	int:
    if latents is None:
       _A      =       randn_tensor(__lowerCAmelCase			,						generator=__lowerCAmelCase			,						device=__lowerCAmelCase			,						dtype=__lowerCAmelCase      )
    else:
       if latents.shape != shape:
          raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''      )
       _A      =       latents.to(__lowerCAmelCase      )

    _A      =       latents * scheduler.init_noise_sigma
    return latents

 def     snake_case_      (    self					:					str			,						__lowerCAmelCase					:					List[Any]=0      )   ->	Optional[Any]:
    if is_accelerate_available():
       from accelerate import cpu_offload
    else:
       raise ImportError('''Please install accelerate via `pip install accelerate`'''      )

    _A      =       torch.device(f'''cuda:{gpu_id}'''      )

    _A      =       [self.image_encoder, self.prior]
    for cpu_offloaded_model in models:
       if cpu_offloaded_model is not None:
          cpu_offload(__lowerCAmelCase			,						__lowerCAmelCase      )

 @property
 def     snake_case_      (    self					:					int      )   ->	List[Any]:
    if self.device != torch.device('''meta'''      ) or not hasattr(self.image_encoder			,						'''_hf_hook'''      ):
       return self.device
    for module in self.image_encoder.modules():
       if (
           hasattr(__lowerCAmelCase			,						'''_hf_hook'''      )
           and hasattr(module._hf_hook			,						'''execution_device'''      )
           and module._hf_hook.execution_device is not None
       ):
          return torch.device(module._hf_hook.execution_device      )
    return self.device

 def     snake_case_      (    self					:					List[str]			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					Optional[Any]			,						__lowerCAmelCase					:					List[Any]			,						__lowerCAmelCase					:					Dict			,						)   ->	Optional[int]:
    if isinstance(__lowerCAmelCase			,						__lowerCAmelCase      ) and isinstance(image[0]			,						torch.Tensor      ):
       _A      =       torch.cat(__lowerCAmelCase			,						axis=0      ) if image[0].ndim == 4 else torch.stack(__lowerCAmelCase			,						axis=0      )

    if not isinstance(__lowerCAmelCase			,						torch.Tensor      ):
       _A      =       self.image_processor(__lowerCAmelCase			,						return_tensors='''pt'''      ).pixel_values[0].unsqueeze(0      )

    _A      =       image.to(dtype=self.image_encoder.dtype			,						device=__lowerCAmelCase      )

    _A      =       self.image_encoder(__lowerCAmelCase      )['''last_hidden_state''']
    _A      =       image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

    _A      =       image_embeds.repeat_interleave(__lowerCAmelCase			,						dim=0      )

    if do_classifier_free_guidance:
       _A      =       torch.zeros_like(__lowerCAmelCase      )

       # For classifier free guidance, we need to do two forward passes.
       # Here we concatenate the unconditional and text embeddings into a single batch
       # to avoid doing two forward passes
       _A      =       torch.cat([negative_image_embeds, image_embeds]      )

    return image_embeds

 @torch.no_grad()
 @replace_example_docstring(__lowerCAmelCase      )
 def __call__(    self					:					str			,						__lowerCAmelCase					:					Union[PIL.Image.Image, List[PIL.Image.Image]]			,						__lowerCAmelCase					:					int = 1			,						__lowerCAmelCase					:					int = 25			,						__lowerCAmelCase					:					Optional[Union[torch.Generator, List[torch.Generator]]] = None			,						__lowerCAmelCase					:					Optional[torch.FloatTensor] = None			,						__lowerCAmelCase					:					float = 4.0			,						__lowerCAmelCase					:					int = 64			,						__lowerCAmelCase					:					Optional[str] = "pil"			,						__lowerCAmelCase					:					bool = True			,						)   ->	Tuple:

    if isinstance(__lowerCAmelCase			,						PIL.Image.Image      ):
       _A      =       1
    elif isinstance(__lowerCAmelCase			,						torch.Tensor      ):
       _A      =       image.shape[0]
    elif isinstance(__lowerCAmelCase			,						__lowerCAmelCase      ) and isinstance(image[0]			,						(torch.Tensor, PIL.Image.Image)      ):
       _A      =       len(__lowerCAmelCase      )
    else:
       raise ValueError(
           f'''`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(__lowerCAmelCase      )}'''      )

    _A      =       self._execution_device

    _A      =       batch_size * num_images_per_prompt

    _A      =       guidance_scale > 1.0
    _A      =       self._encode_image(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase      )

    # prior

    self.scheduler.set_timesteps(__lowerCAmelCase			,						device=__lowerCAmelCase      )
    _A      =       self.scheduler.timesteps

    _A      =       self.prior.config.num_embeddings
    _A      =       self.prior.config.embedding_dim

    _A      =       self.prepare_latents(
        (batch_size, num_embeddings * embedding_dim)			,						image_embeds.dtype			,						__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						self.scheduler			,						)

    # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
    _A      =       latents.reshape(latents.shape[0]			,						__lowerCAmelCase			,						__lowerCAmelCase      )

    for i, t in enumerate(self.progress_bar(__lowerCAmelCase      )      ):
       # expand the latents if we are doing classifier free guidance
       _A      =       torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents
       _A      =       self.scheduler.scale_model_input(__lowerCAmelCase			,						__lowerCAmelCase      )

       _A      =       self.prior(
           __lowerCAmelCase			,						timestep=__lowerCAmelCase			,						proj_embedding=__lowerCAmelCase			,						).predicted_image_embedding

       # remove the variance
       _A  ,    _A      =       noise_pred.split(
           scaled_model_input.shape[2]			,						dim=2      )  # batch_size, num_embeddings, embedding_dim

       if do_classifier_free_guidance is not None:
          _A  ,    _A      =       noise_pred.chunk(2      )
          _A      =       noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

       _A      =       self.scheduler.step(
           __lowerCAmelCase			,						timestep=__lowerCAmelCase			,						sample=__lowerCAmelCase			,						).prev_sample

    if output_type == "latent":
       return ShapEPipelineOutput(images=__lowerCAmelCase      )

    _A      =       []
    for i, latent in enumerate(__lowerCAmelCase      ):
       print()
       _A      =       self.renderer.decode(
           latent[None, :]			,						__lowerCAmelCase			,						size=__lowerCAmelCase			,						ray_batch_size=40_96			,						n_coarse_samples=64			,						n_fine_samples=1_28			,						)

       images.append(__lowerCAmelCase      )

    _A      =       torch.stack(__lowerCAmelCase      )

    if output_type not in ["np", "pil"]:
       raise ValueError(f'''Only the output types `pil` and `np` are supported not output_type={output_type}'''      )

    _A      =       images.cpu().numpy()

    if output_type == "pil":
       _A      =       [self.numpy_to_pil(__lowerCAmelCase      ) for image in images]

    # Offload last model to CPU
    if hasattr(self			,						'''final_offload_hook'''      ) and self.final_offload_hook is not None:
       self.final_offload_hook.offload()

    if not return_dict:
       return (images,)

    return ShapEPipelineOutput(images=__lowerCAmelCase      )









'''simple docstring'''







import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=True      ,UpperCAmelCase="pt"						):






   '''simple docstring'''







   A__       =     {'add_prefix_space': True} if isinstance(UpperCAmelCase      ,UpperCAmelCase						) and not line.startswith(' '						) else {}
   A__       =     padding_side
   return tokenizer(
       [line]      ,max_length=UpperCAmelCase      ,padding='max_length' if pad_to_max_length else None      ,truncation=UpperCAmelCase      ,return_tensors=UpperCAmelCase      ,add_special_tokens=UpperCAmelCase      ,**UpperCAmelCase      ,)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=None      ,):






   '''simple docstring'''







   A__       =     input_ids.ne(UpperCAmelCase						).any(dim=0						)
   if attention_mask is None:
      return input_ids[:, keep_column_mask]
   else:
      return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])

class 	_snake_case(			UpperCAmelCase      ):






 def __init__(self       :  List[str]    ,		a       :  Any    ,		a       :  Union[str, Any]    ,		a       :  int    ,		a       :  Optional[int]    ,		a       :  Optional[Any]="train"    ,		a       :  Optional[int]=None    ,		a       :  Tuple=None    ,		a       :  str=None    ,		a       :  Dict=""    ,		)       ->   Dict:






    """simple docstring"""


    super().__init__()
    A__       =     Path(a    ).joinpath(type_path + '.source'    )
    A__       =     Path(a    ).joinpath(type_path + '.target'    )
    A__       =     self.get_char_lens(self.src_file    )
    A__       =     max_source_length
    A__       =     max_target_length
    assert min(self.src_lens    ) > 0, f"""found empty line in {self.src_file}"""
    A__       =     tokenizer
    A__       =     prefix
    if n_obs is not None:
       A__       =     self.src_lens[:n_obs]
    A__       =     src_lang
    A__       =     tgt_lang






 def __len__(self       :  Optional[Any]    )       ->   List[str]:






    """simple docstring"""


    return len(self.src_lens    )






 def __getitem__(self       :  Any    ,		a       :  str    )       ->   Dict[str, torch.Tensor]:






    """simple docstring"""


    A__       =     index + 1  # linecache starts at 1
    A__       =     self.prefix + linecache.getline(str(self.src_file    )    ,		a    ).rstrip('\n'    )
    A__       =     linecache.getline(str(self.tgt_file    )    ,		a    ).rstrip('\n'    )
    assert source_line, f"""empty source line for index {index}"""
    assert tgt_line, f"""empty tgt line for index {index}"""

    # Need to add eos token manually for T5
    if isinstance(self.tokenizer    ,		a    ):
       source_line += self.tokenizer.eos_token
       tgt_line += self.tokenizer.eos_token

    # Pad source and target to the right
    A__       =     (
        self.tokenizer.question_encoder if isinstance(self.tokenizer    ,		a    ) else self.tokenizer
    )
    A__       =     self.tokenizer.generator if isinstance(self.tokenizer    ,		a    ) else self.tokenizer

    A__       =     encode_line(a    ,		a    ,		self.max_source_length    ,		'right'    )
    A__       =     encode_line(a    ,		a    ,		self.max_target_length    ,		'right'    )

    A__       =     source_inputs['input_ids'].squeeze()
    A__       =     target_inputs['input_ids'].squeeze()
    A__       =     source_inputs['attention_mask'].squeeze()
    return {
        "input_ids": source_ids,
        "attention_mask": src_mask,
        "decoder_input_ids": target_ids,
    }






 @staticmethod
 def 						_UpperCamelCase (a       :  Tuple    )       ->   Tuple:






    """simple docstring"""


    return [len(a    ) for x in Path(a    ).open().readlines()]






 def 						_UpperCamelCase (self       :  str    ,		a       :  int    )       ->   Dict[str, torch.Tensor]:






    """simple docstring"""


    A__       =     torch.stack([x['input_ids'] for x in batch]    )
    A__       =     torch.stack([x['attention_mask'] for x in batch]    )
    A__       =     torch.stack([x['decoder_input_ids'] for x in batch]    )
    A__       =     (
        self.tokenizer.generator.pad_token_id
        if isinstance(self.tokenizer    ,		a    )
        else self.tokenizer.pad_token_id
    )
    A__       =     (
        self.tokenizer.question_encoder.pad_token_id
        if isinstance(self.tokenizer    ,		a    )
        else self.tokenizer.pad_token_id
    )
    A__       =     trim_batch(a    ,		a    )
    A__  ,			A__       =     trim_batch(a    ,		a    ,		attention_mask=a    )
    A__       =     {
        'input_ids': source_ids,
        'attention_mask': source_mask,
        'decoder_input_ids': y,
    }
    return batch




lowerCAmelCase_     						=				getLogger(__name__)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   return list(itertools.chain.from_iterable(UpperCAmelCase						)						)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     get_git_info()
   save_json(UpperCAmelCase      ,os.path.join(UpperCAmelCase      ,'git_log.json'						)						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=4      ,**UpperCAmelCase						):






   '''simple docstring'''







   with open(UpperCAmelCase      ,'w'						) as f:
      json.dump(UpperCAmelCase      ,UpperCAmelCase      ,indent=UpperCAmelCase      ,**UpperCAmelCase						)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   with open(UpperCAmelCase						) as f:
      return json.load(UpperCAmelCase						)







def 						_A      ( ):






   '''simple docstring'''







   A__       =     git.Repo(search_parent_directories=UpperCAmelCase						)
   A__       =     {
       'repo_id': str(UpperCAmelCase						),
       'repo_sha': str(repo.head.object.hexsha						),
       'repo_branch': str(repo.active_branch						),
       'hostname': str(socket.gethostname()						),
   }
   return repo_infos







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   return list(map(UpperCAmelCase      ,UpperCAmelCase						)						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   with open(UpperCAmelCase      ,'wb'						) as f:
      return pickle.dump(UpperCAmelCase      ,UpperCAmelCase						)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   def remove_articles(UpperCAmelCase						):
      return re.sub(r'\b(a|an|the)\b'      ,' '      ,UpperCAmelCase						)

   def white_space_fix(UpperCAmelCase						):
      return " ".join(text.split()						)

   def remove_punc(UpperCAmelCase						):
      A__       =     set(string.punctuation						)
      return "".join(ch for ch in text if ch not in exclude						)

   def lower(UpperCAmelCase						):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase						)						)						)						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     normalize_answer(UpperCAmelCase						).split()
   A__       =     normalize_answer(UpperCAmelCase						).split()
   A__       =     Counter(UpperCAmelCase						) & Counter(UpperCAmelCase						)
   A__       =     sum(common.values()						)
   if num_same == 0:
      return 0
   A__       =     1.0 * num_same / len(UpperCAmelCase						)
   A__       =     1.0 * num_same / len(UpperCAmelCase						)
   A__       =     (2 * precision * recall) / (precision + recall)
   return fa







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   return normalize_answer(UpperCAmelCase						) == normalize_answer(UpperCAmelCase						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   assert len(UpperCAmelCase						) == len(UpperCAmelCase						)
   A__       =     0
   for hypo, pred in zip(UpperCAmelCase      ,UpperCAmelCase						):
      em += exact_match_score(UpperCAmelCase      ,UpperCAmelCase						)
   if len(UpperCAmelCase						) > 0:
      em /= len(UpperCAmelCase						)
   return {"em": em}







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   return model_prefix.startswith('rag'						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     {p: p for p in extra_params}
   # T5 models don't have `dropout` param, they have `dropout_rate` instead
   A__       =     'dropout_rate'
   for p in extra_params:
      if getattr(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):
         if not hasattr(UpperCAmelCase      ,UpperCAmelCase						) and not hasattr(UpperCAmelCase      ,equivalent_param[p]						):
            logger.info('config doesn\'t have a `{}` attribute'.format(UpperCAmelCase						)						)
            delattr(UpperCAmelCase      ,UpperCAmelCase						)
            continue
         A__       =     p if hasattr(UpperCAmelCase      ,UpperCAmelCase						) else equivalent_param[p]
         setattr(UpperCAmelCase      ,UpperCAmelCase      ,getattr(UpperCAmelCase      ,UpperCAmelCase						)						)
         delattr(UpperCAmelCase      ,UpperCAmelCase						)
   return hparams, config


